Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Nonparametric Online Regression while Learning the Metric
Authors: Ilja Kuzborskij, Nicolò Cesa-Bianchi
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study algorithms for online nonparametric regression that learn the directions along which the regression function is smoother. Our algorithm learns the Mahalanobis metric based on the gradient outer product matrix G of the regression function (automatically adapting to the effective rank of this matrix), while simultaneously bounding the regret on the same data sequence in terms of the spectrum of G. As a preliminary step in our analysis, we extend a nonparametric online learning algorithm by Hazan and Megiddo enabling it to compete against functions whose Lipschitzness is measured with respect to an arbitrary Mahalanobis metric. |
| Researcher Affiliation | Academia | Ilja Kuzborskij EPFL Switzerland EMAIL o Cesa-Bianchi Dipartimento di Informatica Universit a degli Studi di Milano Milano 20135, Italy EMAIL |
| Pseudocode | Yes | Algorithm 1 Nonparametric online regression |
| Open Source Code | No | The paper does not provide any specific links or statements about the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper focuses on theoretical analysis and does not specify the use of any particular public or open dataset for training. It discusses a theoretical setup where 'instances xt are realizations of i.i.d. random variables Xt drawn according to some fixed and unknown distribution µ'. |
| Dataset Splits | No | The paper does not provide specific details on train/validation/test dataset splits, as it is a theoretical work. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include concrete details about an experimental setup, such as hyperparameter values or training configurations. |